vllm-omni/examples/offline_inference/qwen2_5_omni

Qwen2.5-Omni: Offline inference

Setup

Please refer to the stage configuration documentation to configure memory allocation appropriately for your hardware setup.

Run examples

Multiple Prompts

Get into the example folder

cd examples/offline_inference/qwen2_5_omni

Then run the command below. Note: for processing large volume data, it uses py_generator mode, which will return a python generator from Omni class.

bash run_multiple_prompts.sh

Single Prompt

Get into the example folder

cd examples/offline_inference/qwen2_5_omni

Then run the command below.

bash run_single_prompt.sh

Modality control

If you want to control output modalities, e.g. only output text, you can run the command below:

python end2end.py --output-wav output_audio \
                  --query-type mixed_modalities \
                  --modalities text

Using Local Media Files

The end2end.py script supports local media files (audio, video, image) via CLI arguments:

# Use single local media files
python end2end.py --query-type use_image --image-path /path/to/image.jpg
python end2end.py --query-type use_video --video-path /path/to/video.mp4
python end2end.py --query-type use_audio --audio-path /path/to/audio.wav

# Combine multiple local media files
python end2end.py --query-type mixed_modalities \
    --video-path /path/to/video.mp4 \
    --image-path /path/to/image.jpg \
    --audio-path /path/to/audio.wav

# Use audio from video file
python end2end.py --query-type use_audio_in_video --video-path /path/to/video.mp4

If media file paths are not provided, the script will use default assets. Supported query types:

• use_image: Image input only
• use_video: Video input only
• use_audio: Audio input only
• mixed_modalities: Audio + image + video
• use_audio_in_video: Extract audio from video
• text: Text-only query

Composable parallelism (strategy configs)

You can shard or replicate a stage with a small composable-parallel strategy.yaml overlaid onto the bundled default deploy config -- no bespoke deploy YAML required. Supply the strategy with --strategy-config and any matching device layout via --stage-overrides (explicit kwargs forwarded to Omni).

Both examples below require >= 3 GPUs (the thinker uses 2, talker + code2wav share a third):

# Tensor-parallel the thinker (TP=2)
python end2end.py --query-type text --num-prompts 6 \
    --strategy-config strategy_tp2.yaml \
    --stage-overrides '{"0": {"devices": "0,1"}, "1": {"devices": "2"}, "2": {"devices": "2"}}'

# Replicate the thinker across 2 engines (round-robin load balancing)
python end2end.py --query-type text --num-prompts 6 \
    --strategy-config strategy_stage_replica.yaml \
    --stage-overrides '{"0": {"devices": "0,1"}, "1": {"devices": "2"}, "2": {"devices": "2"}}'